# coding=utf-8 # Copyright 2020 HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from tests.test_configuration_common import ConfigTester from tests.test_modeling_tf_bart import TFBartModelTester from tests.test_modeling_tf_common import TFModelTesterMixin from transformers import BlenderbotConfig, BlenderbotSmallTokenizer, is_tf_available from transformers.file_utils import cached_property from transformers.testing_utils import is_pt_tf_cross_test, require_tf, require_tokenizers, slow if is_tf_available(): import tensorflow as tf from transformers import TFAutoModelForSeq2SeqLM, TFBlenderbotForConditionalGeneration class TFBlenderbotModelTester(TFBartModelTester): config_updates = dict( normalize_before=True, static_position_embeddings=True, do_blenderbot_90_layernorm=True, normalize_embeddings=True, ) config_cls = BlenderbotConfig @require_tf class TFBlenderbotModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else () all_generative_model_classes = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else () is_encoder_decoder = True test_pruning = False def setUp(self): self.model_tester = TFBlenderbotModelTester(self) self.config_tester = ConfigTester(self, config_class=BlenderbotConfig) def test_config(self): self.config_tester.run_common_tests() def test_inputs_embeds(self): # inputs_embeds not supported pass def test_saved_model_with_hidden_states_output(self): # Should be uncommented during patrick TF refactor pass def test_saved_model_with_attentions_output(self): # Should be uncommented during patrick TF refactor pass def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) x = model.get_output_layer_with_bias() assert x is None name = model.get_prefix_bias_name() assert name is None @is_pt_tf_cross_test @require_tokenizers class TFBlenderbot90MIntegrationTests(unittest.TestCase): src_text = [ "Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like i'm going to throw up.\nand why is that?" ] model_name = "facebook/blenderbot-90M" @cached_property def tokenizer(self): return BlenderbotSmallTokenizer.from_pretrained(self.model_name) @cached_property def model(self): model = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name, from_pt=True) return model @slow def test_90_generation_from_long_input(self): model_inputs = self.tokenizer(self.src_text, return_tensors="tf") generated_ids = self.model.generate( model_inputs.input_ids, attention_mask=model_inputs.attention_mask, num_beams=2, use_cache=True, ) generated_words = self.tokenizer.batch_decode(generated_ids.numpy(), skip_special_tokens=True)[0] assert generated_words in ( "i don't know. i just feel like i'm going to throw up. it's not fun.", "i'm not sure. i just feel like i've been feeling like i have to be in a certain place", "i'm not sure. i just feel like i've been in a bad situation.", )